How can organisations measure whether AI-assisted coding is improving software delivery without increasing risk?

Track both velocity and control outcomes. Useful signals include the percentage of AI-generated code that is reviewed, the rate of security defects found after merge, the number of secrets or vulnerable patterns introduced through suggestions, and the time needed to remediate them. If speed rises while defects and rework stay flat or fall, the programme is working.

Why This Matters for Security Teams

AI-assisted coding changes the balance between delivery speed and control assurance. The useful question is not whether developers are using code suggestions, but whether those suggestions are improving throughput without expanding the attack surface. Security teams need measures that capture both software delivery performance and the quality of guardrails around code review, dependency choice, secret handling, and test coverage. The NIST Cybersecurity Framework 2.0 is a helpful anchor because it treats governance, protection, detection, response, and recovery as linked outcomes rather than isolated checks.

Teams often get misled by vanity metrics such as lines of code produced or pull requests closed. Those numbers can rise even when AI-generated suggestions increase rework, introduce insecure defaults, or create subtle supply chain risk through copied snippets and weak dependency choices. The better measure is whether engineering can sustain higher delivery rate while the control environment remains stable or improves. That means security leaders should watch both adoption and assurance, then test whether the change is durable across teams, repositories, and release cycles. In practice, many security teams encounter the real risk only after a vulnerable suggestion or leaked secret has already reached a shared branch, rather than through intentional measurement design.

How It Works in Practice

Measurement works best when it combines engineering metrics with security outcome metrics. Start by defining a baseline before broad AI coding adoption, then compare like for like across similar services or teams. Good programmes segment by application criticality, language, and repository maturity, because AI assistance has very different effects in greenfield prototypes versus regulated production systems.

Useful measures usually include:

• Percent of AI-generated code that receives human review before merge.
• Rate of security defects found after merge, including those identified by SAST, code review, and penetration testing.
• Count of secrets, unsafe functions, or vulnerable patterns introduced through AI suggestions.
• Mean time to remediate AI-introduced issues.
• Change in deployment frequency, lead time for changes, and rollback rate.

Security teams should also examine whether AI outputs are being validated against policy. For example, code assistants can speed up scaffolding, but they may also normalise weak auth patterns, unsafe deserialisation, or hardcoded credentials if guardrails are not enforced. Controls from NIST SP 800-53 Rev 5 Security and Privacy Controls remain relevant here, especially where secure development, configuration management, and access restrictions need to be translated into CI/CD checks and repository policy. The practical test is whether AI assistance reduces cycle time without increasing the number of security exceptions that require manual intervention. Teams should also review whether model prompts, code generation settings, and approved usage policies are documented, because governance gaps often hide in informal developer workflows.

When the programme is mature, trend analysis matters more than one-off audits. Look for whether the same classes of defects recur, whether review effort shifts from syntax fixes to security judgement, and whether productivity gains hold after the first few months of adoption. These controls tend to break down in distributed monorepos with inconsistent review discipline because the signal from AI-assisted changes becomes too noisy to separate from general engineering churn.

Common Variations and Edge Cases

Tighter measurement often increases process overhead, requiring organisations to balance delivery speed against the cost of extra review, tagging, and telemetry. That tradeoff is real, especially where teams want lightweight adoption but also need evidence for risk committees, auditors, or regulated customers.

Current guidance suggests there is no universal standard for measuring AI-assisted coding performance yet, so organisations should avoid copying a single benchmark across all teams. For low-risk internal tools, it may be enough to track defect rates and remediation time. For customer-facing or regulated workloads, the bar is higher and should include change approval quality, secret scanning, dependency provenance, and evidence that AI-generated code is subject to the same secure development controls as human-written code. If a team uses AI for test generation, the metric set should also confirm whether test coverage is meaningful or merely inflated by low-value assertions.

Edge cases matter when the software stack includes generated infrastructure code, multi-repo release pipelines, or agents that can modify code automatically. In those environments, AI-assisted coding starts to overlap with broader software supply chain risk, so measurement should extend beyond the editor into build systems, artifact signing, and release governance. That is where delivery metrics alone become misleading, because faster code creation can mask slower control validation.

Related resources from NHI Mgmt Group

• How do teams decide whether AI adoption is increasing security risk or improving control?
• How can organisations reduce risk without stopping AI-assisted development?
• How can organisations tell whether AI-assisted remediation is actually reducing risk?
• How can organisations tell whether AI is improving code quality or just increasing output?